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相关概念视频

Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Introduction and Methods of Leveling01:26

Introduction and Methods of Leveling

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Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

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Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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相关实验视频

Updated: Sep 14, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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Published on: June 13, 2025

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增强图形重建:将双层图形结构与图形强化学习相结合.

Dazi Li, Yanyang Bao, Xin Xu

    IEEE transactions on neural networks and learning systems
    |July 24, 2025
    PubMed
    概括

    本研究引入了一种新的图形强化学习 (GRL) 方法,用于组合优化问题,如工作室调度. 该方法通过使用图表注意力网络和双层图表表示来提高解决方案质量和概括性.

    科学领域:

    • 人工智能的人工智能
    • 运营研究 运营研究
    • 机器学习 机器学习

    背景情况:

    • 组合优化通常被视为一维排序问题,通过维度压缩丢失有价值的信息.
    • 使用卷积神经网络 (CNN) 的传统强化学习 (RL) 难以将关系信息捕获到特征矩阵中.

    研究的目的:

    • 解决现有的RL方法对组合优化的局限性.
    • 为工作车间安排问题 (JSSP) 提出一种新的方法,克服CNN的约束.

    主要方法:

    • 从图形重建的角度重新构建了这个问题.
    • 开发了一个图形RL (GRL) 方法,集成双深Q网络 (DDQN) 和图形注意力网络 (GAT).
    • 构建了一个双层图表表示,以学习调度功能和处理动态图.

    主要成果:

    • 与传统的深度RL (DRL) 算法相比,提出的GRL方法证明了解决方案质量的提高.
    • 在实验结果中观察到增强的概括性能.
    • 双层图形表示有效地捕获复杂的调度信息.

    结论:

    • 在组合优化方面,GRL方法在基于CNN的RL上提供了显著的进步.

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  • 这种方法提供了一种更有效的方式来学习和解决复杂的调度问题.